• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.4
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• pp.705-711
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• 2003

An automatic face and facial feature points(FFPs) detection system is proposed. A face is represented as a graph where the nodes are placed at facial feature points(FFPs) labeled by their Gabor features and the edges are describes their spatial relations. An innovative flexible feature matching is proposed to perform features correspondence between models and the input image. This matching model works likes random diffusion process in !be image space by employing the locally competitive and globally corporative mechanism. The system works nicely on the face images under complicated background, pose variations and distorted by facial accessories. We demonstrate the benefits of our approach by its implementation on the face identification system.

• Anxiety and mood
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• v.10 no.1
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• pp.37-43
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• 2014

Objective : Social Phobia is associated with extensive disability and reduced quality of life. The concept of 'social self' is a representation of the self-reflected in the eyes of others, and is recruited during self-face recognition, which is closely related to self-esteem. The aim of this study was to identify the relationship of face recognition for self and others using measures of social anxiety and self-esteem in patients with social phobia. Methods : Twenty-seven patients with social phobia and twenty-three normal controls were evaluated with scales of self-esteem, depression, anxiety and other psychiatric symptoms. All participants completed the self-face recognition task. Nine self-faces, nine other faces and eighty-one morphed faces were presented randomly for each trial. The participants were instructed to make a decision as to whether the stimuli were self-face or not. The responses and reaction times were recorded during the task. Results : There were no group differences of the morphing composition at the recognition start point as self-face. In patients with social phobia, the mean reaction time at the start point of recognizing as a self-face was 1,037.6 ms, which was significantly longer than that of normal controls (911.3 ms, p<0.05). Patients with social phobia showed a significant negative correlation between the mean reaction time and the severity of depression when the stimuli were recognized as a self-face (r=-0.421, p<0.05). Conclusion : A difficulty in attention rather than avoidance may be an important factor of face recognition in patients with social phobia. When considering self-face recognition in such patients, many factors, such as anxiety, depression, working memory and theory of mind, need to be considered.

• Journal of the Korea Computer Industry Society
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• v.2 no.10
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• pp.1291-1300
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• 2001

This article come to study the visualization representation technique of eye brain of person, basing on the ground of the dynamic graphics which is able to change the real time, manipulating the image as graphic factors of the multi-data. And the important thought in such realization is as follows ; corresponding the character points of human face and the parameter control value which obtains basing on the existing image recognition algorithm to the multi-dimensional data, synthesizing the image, it is to create the virtual image from the emotional expression according to the changing contraction expression. The proposed DyFIG system is realized that it as the completing module and we suggest the module of human face graphics which is able to express the emotional expression by manipulating and experimenting, resulting in realizing the emotional data expression description and technology.

• Journal of KIISE:Software and Applications
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• v.32 no.8
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• pp.789-794
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• 2005

LDA (Linear Discriminant Analysis) provides the projection that discriminates the data well, and shows a very good performance for face recognition. However, since LDA provides only one transformation matrix over whole data, it is not sufficient to discriminate the complex data consisting of many classes like honan faces. To overcome this weakness, we propose a new face recognition method, called LDA mixture model, that the set of alf classes are partitioned into several clusters and we get a transformation matrix for each cluster. This detailed representation will improve the classification performance greatly. In the simulation of face recognition, LDA mixture model outperforms PCA, LDA, and PCA mixture model in terms of classification performance.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.203-207
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• 2001

In this Paper, we propose the neural network based emotion recognition method for intelligently recognizing the human's emotion using CCD color image. To do this, we first acquire the color image from the CCD camera, and then propose the method for recognizing the expression to be represented the structural correlation of man's feature Points(eyebrows, eye, nose, mouse) It is central technology that the Process of extract, separate and recognize correct data in the image. for representation is expressed by structural corelation of human's feature Points In the Proposed method, human's emotion is divided into four emotion (surprise, anger, happiness, sadness). Had separated complexion area using color-difference of color space by method that have separated background and human's face toughly to change such as external illumination in this paper. For this, we propose an algorithm to extract four feature Points from the face image acquired by the color CCD camera and find normalization face picture and some feature vectors from those. And then we apply back-prapagation algorithm to the secondary feature vector. Finally, we show the Practical application possibility of the proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.133-138
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• 2008

In this paper, we propose a novel anchor shot detection system, named to MASD (Multi-phase Anchor Shot Detection), which is a core step of the preprocessing process for the news video analysis. The proposed system is composed of four modules and operates sequentially: 1) skin color detection module for reducing the candidate face regions; 2) face detection module for finding the key-frames with a facial data; 3) vector representation module for the key-frame images using a non-negative matrix factorization; 4) one class SVM module for determining the anchor shots using a support vector data description. Besides the qualitative analysis, our experiments validate that the proposed system shows not only the comparable accuracy to the recently developed methods, but also more faster detection rate than those of others.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.6
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• pp.759-764
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• 2005

In this paper we describe a face recognition using EBGM(Elastic Bunch Graph Matching) method. Usally, the PCA and LDA based face recognition method with the low-dimensional subspace representation use holistic image of faces, but this study uses local features such as a set of convolution coefficients for Gabor kernels of different orientations and frequencies at fiducial points including the eyes, nose and mouth. At pre-recognition step, all images are represented with same size face graphs and they are used to recognize a face comparing with each similarity for all images. The proposed algorithm has less computation time due to simplified face graph than conventional EBGM method and the fuzzy matching method for calculating the similarity of face graphs renders more face recognition results.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.1
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• pp.35-43
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• 2010

1D PCA utilized in the face appearance-based face recognition methods such as eigenface-based face recognition method may lead to less face representative power and more computational cost due to the resulting 1D face appearance data vector of high dimensionality. To resolve such problems of 1D PCA, 2D PCA-based face recognition methods had been developed. However, the face representation model obtained by direct application of 2D PCA to a face image set includes both face common features and face distinctive identity features. Face common features not only prevent face recognizability but also cause more computational cost. In this paper, we first develope a model of a face distinctive identity feature subspace separated from the effects of face common features in the face feature space obtained by application of 2D PCA analysis. Then, a novel robust face recognition based on the face distinctive identity feature subspace model is proposed. The proposed face recognition method based on the face distinctive identity feature subspace shows better performance than the conventional PCA-based methods (1D PCA-based one and 2D PCA-based one) with respect to recognition rate and processing time since it depends only on the face distinctive identity features. This is verified through various experiments using Yale A and IMM face database consisting of face images with various face poses under various illumination conditions.

• Journal of the Korea Computer Graphics Society
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• v.16 no.2
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• pp.29-37
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• 2010

In this paper, we present a novel deformation method for alleviating the asymmetry of a scanned 3D face considering facial features. To handle detailed areas of the face, we developed a new local 3D shape descriptor based on facial features and surface curvatures. Our shape descriptor can improve the accuracy when deforming a 3D face toward a symmetric configuration, because it provides accurate point pairing with respect to the plane of symmetry. In addition, we use point-based representation over all stages of symmetrization, which makes it much easier to support discrete processes. Finally, we performed a statistical analysis to assess subjects' preference for the symmetrized faces by our approach.

• Education of Primary School Mathematics
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• v.16 no.3
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• pp.285-301
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• 2013

The purpose of the study is to analyze the 4th graders' visual representation in mathematics problem solving process and to find out how to teach the visual representation in mathematics problem solving process. on the basis of the results, this study gives several pedagogical implication related to the mathematics problem solving. The following were the conclusions drawn from the results obtained in this study. First, The achievement level of students and using visual representation in the mathematics problem solving are closely connected. High achieving students used visual representation in the mathematics problem solving process more frequently. Second, high achieving students realize the usefulness of visual representation in the mathematics problem solving process and use visual representation to solve mathematical problem. But low achieving students have no conception that visual representation is one of the method to solve mathematical problem. Third, students tend to especially focus on 'setting up an equation' when they solve a mathematical problem. Because they mostly experienced mathematical problems presented by the type of 'word problem-equation-answer'. Fourth even through students tried visual representation to solve a mathematical problem, they could not solve the problem successfully in numerous instances. Because students who face a difficulty in solving a problem try to construct perfect drawing immediately. But generating visual representation 2)to represent mathematical problem cannot be constructed at one swoop.